{"id":"W4313309314","doi":"10.1101/2022.12.14.22283419","title":"Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Telus (Canada); Queen's University; Roche (Canada); Vector Institute; University Health Network; University of Toronto","funders":"","keywords":"Social media; Anxiety; Headaches; Medicine; Coronavirus disease 2019 (COVID-19); Distress; Psychology; Disease; Psychiatry; Clinical psychology; Infectious disease (medical specialty); Computer science; Pathology; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001565129,0.0004664953,0.001078749,0.0005068379,0.0006863578,0.0001163105,0.0003463636,0.0003416222,0.0001984572],"category_scores_gemma":[0.0007866956,0.0003935528,0.0003059444,0.000742195,0.0001346127,0.0000678658,0.0008796776,0.002905033,0.000005760444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001530869,"about_ca_system_score_gemma":0.00295436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001634375,"about_ca_topic_score_gemma":0.00004930526,"domain_scores_codex":[0.9948097,0.0005325947,0.0009775823,0.001051177,0.001834877,0.0007940601],"domain_scores_gemma":[0.9978576,0.0001141723,0.0005706545,0.0006912588,0.0001965603,0.0005697975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.004496895,0.003547617,0.3608958,0.01857776,0.00402459,0.01562751,0.4190153,0.00543806,0.003995956,0.0001337135,0.004428898,0.1598179],"study_design_scores_gemma":[0.02113921,0.003041037,0.479629,0.007006775,0.003228608,0.002440906,0.4234661,0.03508224,0.001616262,0.0006916783,0.01426339,0.008394795],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986113,0.005980585,0.001940283,0.001966058,0.0008002762,0.001911877,0.00003751778,0.0002205954,0.001029842],"genre_scores_gemma":[0.9956927,0.00002483712,0.001105317,0.002019614,0.0003485215,0.0001243248,0.0002696782,0.0001039025,0.0003110603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1514231,"threshold_uncertainty_score":0.9998516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1661532929294269,"score_gpt":0.4129938952276986,"score_spread":0.2468406022982717,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}